基于BP神经网络的建筑工程造价的快速预测研究
Research on Fast Prediction of Construction Engineering Cost Based on BP Neural Network
刘芳1
作者信息
- 1. 上海市政工程设计研究总院(集团)有限公司,上海 200000
- 折叠
摘要
本文通过分析建筑工程造价影响因素,选取了包括基础类型、结构类型、工程总层数等在内的 8 个主要特征因素,并对这些特征因素进行量化处理,构建了影响工程造价的特征向量.同时,本文基于灰色系统理论,运用灰色预测和灰色系统模型对数据进行处理,提高了数据的准确性和可靠性.基于此,本文结合BP神经网络算法,实现了对建筑工程造价的快速预测.通过反复调试和对比,确定了合适的网络参数,最终实现了对建筑工程造价的有效预测.
Abstract
This article analyzes the factors affecting the cost of construction projects,selects 8 main characteristic factors including foundation type,structural type,and total number of floors of the project,and quantifies these characteristic factors to construct a feature vector that affects the cost of the project.Meanwhile,based on grey system theory,this article uses grey prediction and grey system models to process data,improving the accuracy and reliability of the data.Based on this,this article combines the BP neural network algorithm to achieve rapid prediction of construction project costs.Through repeated debugging and comparison,suitable network parameters were determined,and effective prediction of construction project cost was ultimately achieved.
关键词
BP神经网络/灰色系统/建筑工程造价/造价预测Key words
BP neural network/grey system/construction project cost/cost prediction引用本文复制引用
出版年
2025
NETL
NSTL
万方数据